Assume we have a base image \(x\) . Input it to the pre-trained feature extractor \(\mathcal {J}(\cdot )\)  and achieve the feature vector \(\mathbf {x}=\mathcal {J}(x) \in \mathbb {R}^{dim}\) , where \(dim\)  denotes the dimension of the feature vector.
Next, input the feature into the base classifier \(\mathcal {M}_{b}(\cdot )\)  to predict the soft label. There are lots of choices, such as Support Vector Machine [1]}, Nearest Neighbor [2]}, Softmax [3]}.
In the standard classification task, we select the softmax activation function as our classifier. Specifically, we project the feature vector into a label space, i.e., \(\mathbf {x} \rightarrow \mathbf {z}^{sc}\) , where \(\mathbf {z}^{sc} = [z^{sc}_1,z^{sc}_2,\cdots ,z^{sc}_{C_{b}}] \in \mathbb {R}^{C_{b}}\) , \(C_{b}\)  denotes the number of base category. Then transform it to the probability distribution by:
\(\begin{split}y^{sc}_c = \frac{e^{z^{sc}_c}}{\sum _{c=1}^{C_{b}} e^{z^{sc}_c}}\end{split}\) 
